In summary, we made the explicit analogy from agents and swarms to neurons and
neural circuits. This analogy permitted the tools of theoretical neuroscience
to be leveraged in developing a model artificial spatial system. The
NeuroSwarms controller required two features to support cognitive swarming:
(1) an internal phase state, and (2) decoupling of physical location from
internal self-localization. The phase state naturally encapsulated neural
activation (cf. equation (REF )) and could be used to drive spike generation,
if desired, in future models. Phase-based organization additionally
leveraged the expressive complexity of mobile oscillators revealed by the
swarmalator formalism [1]}, [2]}. The separation
of position vs. self-localization allowed swarm motion dynamics to be
interpreted as Hebbian learning in an oscillatory place-coding neural network
(Section REF ). Thus, theorized hippocampal phenomena such as
attractor map formation and oscillatory sequence generation provide a framework
for advances in decentralized swarm control and, reciprocally, the theoretical
neuroscience of spatial navigation in complex, changing environments.
